Revealing proteome-level functional redundancy in the human gut microbiome using ultra-deep metaproteomics

Li, Leyuan, Wang, Tong, Ning, Zhibin, Zhang, Xu, Butcher, James, Serrana, Joeselle M., Simopoulos, Caitlin M.A., Mayne, Janice, Stintzi, Alain, Mack, David R., Liu, Yang Yu and Figeys, Daniel (2023) Revealing proteome-level functional redundancy in the human gut microbiome using ultra-deep metaproteomics. Nature Communications, 14 (1). pp. 1-14. ISSN 2041-1723

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Abstract

Functional redundancy is a key ecosystem property representing the fact that different taxa contribute to an ecosystem in similar ways through the expression of redundant functions. The redundancy of potential functions (or genome-level functional redundancy FR g) of human microbiomes has been recently quantified using metagenomics data. Yet, the redundancy of expressed functions in the human microbiome has never been quantitatively explored. Here, we present an approach to quantify the proteome-level functional redundancy FR p in the human gut microbiome using metaproteomics. Ultra-deep metaproteomics reveals high proteome-level functional redundancy and high nestedness in the human gut proteomic content networks (i.e., the bipartite graphs connecting taxa to functions). We find that the nested topology of proteomic content networks and relatively small functional distances between proteomes of certain pairs of taxa together contribute to high FR p in the human gut microbiome. As a metric comprehensively incorporating the factors of presence/absence of each function, protein abundances of each function and biomass of each taxon, FR p outcompetes diversity indices in detecting significant microbiome responses to environmental factors, including individuality, biogeography, xenobiotics, and disease. We show that gut inflammation and exposure to specific xenobiotics can significantly diminish the FR p with no significant change in taxonomic diversity.

Item Type:	Article
Additional Information:	The authors acknowledge Ruth Singleton (Clinical Research Coordinator) for participant recruitment and data collection. The ultra-deep metaproteomics datasets were deposited to the ProteomeXchange Consortium (http://www.proteomexchange.org) via the PRIDE partner repository with the dataset identifier PXD027297. Database search outputs from the SISPROT23, RapidAIM24, Berberine25 and IBD26 studies have been previously deposited to the ProteomeXchange Consortium with the dataset identifiers PXD005619, PXD012724, PXD015934 and PXD007819, respectively. Proteomics dataset of the cultured singles strain samples (Wang et al.19,) has been previously deposited to the ProteomeXchange Consortium with the dataset identifier PXD037923. The four metagenomic datasets matching the ultra-deep metaproteomics datasets were obtained from the previous MetaPro-IQ study16, accessible from the NCBI sequence read archive (SRA) under the accession of SRP068619. Source data are provided with this paper. Custom codes for the construction of PCN and calculation of FRp are available at GitHub: https://github.com/yvonnelee1988/Metaproteome_FRp.
Uncontrolled Keywords:	chemistry(all),biochemistry, genetics and molecular biology(all),general,physics and astronomy(all),sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/1600
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	09 Mar 2026 17:30
Last Modified:	15 Mar 2026 07:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/102281
DOI:	10.1038/s41467-023-39149-2

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